Essays on Networks, Dictatorships, and Political Violence
- Author(s): Derpanopoulos, George
- Advisor(s): Geddes, Barbara
- et al.
This dissertation contains three essays, each addressing a different question in political economy and comparative politics. The first essay speaks to the large literature arguing that dictatorships can achieve high levels of economic growth if dictators can commit to not expropriate elites. Extant research has focused on the role of formal institutions – legislatures and parties – in helping elites constrain dictators’ predation. I complement this literature by documenting the role of an informal institution, elite financial networks, in constraining the dictator. I argue that dense financial ties among elites diffuse private information on the state of the economy, hence facilitating elites’ monitoring – if the dictator reneges on his commitments to elites, informed elites are able to infer and punish his defection. Accordingly, I hypothesize that dictatorships with denser elite financial networks enjoy stronger property rights. To test my argument, I uncover networks of elites’ co-ownership of offshore companies – a strong type of financial tie – using a large, untapped leak of private financial information, the Panama Papers. A thorough regression analysis of almost all dictatorships in the period 2002−2013 supports my theory: a one-standard deviation increase in financial network density predicts a half-standard deviation decrease in expropriation risk.
The second essay asks: why have some countries counted hundreds of their citizens fleeing to fight in Syria, while other countries’ citizens have remained bystanders? There are three methodological challenges to answering this question. First, there may be two groups of countries: one at no risk of "supplying" foreign fighters and another supplying some positive amount. Second, there is no theory that specifies a functional form linking countries’ features to foreign fighter supply. Third, existing models for predicting foreign fighter supply perform poorly out of sample or yield output that is not amenable to social-scientific interpretations. To solve these challenges, I augment a count regression model, the hurdle negative binomial, with two machine learning tools. Namely, I allow features to affect the response non-parametrically, by using kernel functions that represent expansions of the data. Furthermore, I add regularization terms that penalize complexity to mitigate overfitting. My approach combines the strengths of predictive and confirmatory models: it performs similarly to state-of-the-art machine learning algorithms in prediction while providing substantively interpretable output. Applying the model to data on 163 countries, I find that populous, developed countries, with a large Sunni population and proximity to Syria supply more fighters. These results lend themselves to viewing foreign fighter supply as largely driven by structural forces.
The third essay contributes to the literature on civil war, which has recently shifted its attention from state-rebel violence to rebel-rebel violence. I build on this work by applying tools from social network analysis to visualize, summarize, and model conflict among 22 rebel groups in Lebanon’s Civil War, specifically in the period 1980−1991. Using a network graph and node-, dyad-, and network-level statistics, I find a conflict structure in line with historical accounts: a dense pattern of hostilities, high reciprocity in hostilities (i attacks j ⇔ j attacks i), low transitivity in hostilities (the enemy of my enemy is my friend), infighting within religious sects, and the existence of 3 central groups. Furthermore, using regression models tailored to network data, I find that groups that command support from the ethno-religious sect they belong to, control valuable natural resources and territory, and use terrorist tactics are more likely to attack other rebels, while groups that are able to reach an agreement with the state are less likely to attack other rebels. Finally, using a clustering model, I detect 2 sub-conflicts: a narrow cluster that includes the infighting among Palestinian groups and their Sunni allies and a broader cluster that includes the hostilities between rival Shi’ite groups. My approach is relevant to policy-makers deciding which rebel groups to support, particularly in conflicts where opposition to the state is fragmented.